code: def get_default_rl_module_spec(self) -> "RLModuleSpec":
      if self.framework_str == "tf2":
          from ray.rllib.core.testing.tf.bc_module import (
              DiscreteBCTFModule,
          )
  
          cls = DiscreteBCTFModule
      elif self.framework_str == "torch":
          from ray.rllib.core.testing.torch.bc_module import (
              DiscreteBCTorchModule,
          )
  
          cls = DiscreteBCTorchModule
      else:
          raise ValueError(
              f"Unsupported framework: {self.framework_str}"
          )
  
      spec = SingleAgentRLModuleSpec(
          module_class=cls,
          model_config_dict={"fcnet_hiddens": [32]},
      )
  
      if self.is_multi_agent():
          # TODO (Kourosh): Make this more multi-agent for example with policy ids
          #  "1" and "2".
          return MultiAgentRLModuleSpec(
              marl_module_class=MultiAgentRLModule,
              module_specs={DEFAULT_POLICY_ID: spec},
          )
      else:
          return spec
  
file_path: ray/rllib/core/testing/testing_learner.py
imports: from typing import Any, DefaultDict, Dict, Mapping, Type
  import numpy
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModule, MultiAgentRLModuleSpec
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.nested_dict import NestedDict
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.typing import ModuleID, RLModuleSpec, TensorType
label_desc: Use image node_id 2 for calling the BaseTestingAlgorithmConfig obj's underlying member method code with example usage: obj.get_default_rl_module_spec() and returns: MultiAgentRLModuleSpec, spec